Data Mining Techniques

Data mining is one of the key processes used to sort large sets of data to recognize specific data patterns, and identify useful business insights, and relationships between different parameters. This information can be used by organizations to solve critical business problems and take informed decisions to improve the quality of services and serve customers better.

Data mining is one of the most misunderstood concepts. It's an advanced technique which leverages a wide range of tools, technologies, and computer aided data analysis using huge data sets. This article aims to highlight salient data mining techniques, applications, and its importance for businesses.

7 Most Popular Data Mining Techniques and Applications

Data mining techniques are leveraged in the industry for research purposes in cybernetics, mathematics, genetics, marketing, etc. Being an important application today, there are numerous useful tips for effective data mining from the specialists that help businesses make the most out it. If data mining is utilized in the correct manner, data extraction has the capability to drive high efficiencies and predict the customer behavior in the future and help companies to stay way ahead of their competitors.

Some of the key data mining applications and techniques are listed below -

Association

Application

Association

This data mining technique is used to establish the correlation between two or more items and discover patterns

Application

This method can be used to group products, in-store product placement, and analyze any kind of imperfections

Classification

Application

Classification

This technique is used to identify the classes of products and customers by using the associated attributes

Application

This is very useful when you need to identify which customers are likely to purchase your products, find valuable customers, or customers who respond to a certain type of marketing strategy, etc.

Clustering

Application

Clustering

This data mining method is used to explore the data by applying one or more of the attributes to find any kind of correlations among the cluster members

Application

This is used to identify new customer segments, find similarity in genetic segments, grouping similar sites, segregating population structures, etc.

Outlier Detection

Application

Outlier Detection

This methods is used to identify any kind of suspicious and unusual piece of data which deviates from the normal and set pattern

Application

This method is mainly used to find any kind of anomaly in the identification of tax fraud, credit fraud, etc.

Regression Analysis

Application

Regression Analysis

This technique is used to establish the dependency between two different variables so that their relationship can be used to predict the outcome of any one variable

Application

This method can be used to predict the effect of the real estate market on the GDP, customer lifetime value resulting from loyalty, etc.

Attribute Strength Detection

Application

Attribute Strength Detection

This technique is used to find the association strength of some of the attributes with the target attributes

Application

The main examples of using this method include finding factors associated with the best-performing employees, finding factors which are associated with customers who react to certain marketing strategies, etc.

Feature Selection

Application

Feature Selection

This data mining method is used to create new attributes by making linear combination of the existing attributes

Application

The most common applications of this method are pattern recognition, data compression, latent semantic analysis, etc.

6-Step Data Mining Process

Data mining is a process which needs to be followed in a step-wise manner. This is a simple 6-step process which can help companies to gain deep insights from the company data and also predict the future trends depending on the current data patterns. The steps in the data mining process are shown here -

01. Identifying the Problem Statement

This step involves understanding the project and the business problem. The project objectives and requirements are clearly defined by the data mining experts, domain experts, and the business experts keeping in mind all the aspects of the business

02. Categorizing Data for Analysis

Domain experts will identify, collect, and describe the data that needs to be analyzed and the quality problems associated with the data. This also involves interaction with the business and data mining experts and the use of traditional data analysis tools

03. Model Building

Domain experts will use the available insights and patterns to build a model that predicts future patterns. However, the data needs to be cleaned and formatted, as certain mining functions accept data only in a certain format. Here, the data is prepared for the modeling tool by selecting records, attributes, and tables

04. Data Modeling

The mining functions are selected and applied by the data mining experts. The data mining experts must understand each model and their specific data requirements

05. Result Evaluation

The model is evaluated by the data mining experts and is checked whether it meets the requirements. If the results are not satisfactory, then the models are rebuilt with changed parameters until the desired values are obtained

06. Final Deployment

After the final modeling, if the results are found to be satisfactory, then they are exported to database tables or into applications for deployment

Business Benefits of Data Mining

Data mining mainly focuses on extracting meaningful information from large sets of data and transforming it into an easily interpretable format for further analysis. Businesses can leverage the power of data mining to improve their business processes. Businesses can use handy tips for successful data mining across different industry verticals, and make their business a success.

Retail stores can utilize data mining results to tweak their store layouts depending on the customers' shopping or buying patterns, thereby ensuring improved client experience, which will ultimately lead to increased revenue

Manufacturing companies can use the results to improve their products' safety, comfort, and usability

In finance and banking sector, data mining can be utilized to create risk models for mortgages and loans, as well as to detect fraudulent transactions

In marketing, data mining techniques can be used to enhance client satisfaction, improve conversions, and create targeted campaigns. They can also use it to study new products and think of new product ideas after analyzing the historical data

Government bodies can use these methods to detect any kind of fraudulent transactions and find suspicious government documents and tax returns

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